A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model

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Abstract

Broadband short-wave (SW) surface direct and diffuse irradiances are not typically within the set of output variables produced by numerical weather prediction (NWP) models. However, they are frequently requested for solar energy applications. In order to compute them, a detailed representation of the aerosol optical properties is important. Nonetheless, NWP models typically oversimplify aerosol representation or even neglect their effect. In this work, a flexible method to account for the SW aerosol optical properties in the computation of broadband SW surface direct and diffuse irradiances is presented. It only requires aerosol optical depth at 0.55 μm and knowledge of the type of predominant aerosol. Other parameters needed to consider spectral aerosol extinction, namely, Angström exponent, aerosol single-scattering albedo and aerosol asymmetry factor, are parameterized. The parameterization has been tested using the Rapid Radiative Transfer Model for climate and weather models (RRTMG) SW scheme of the Weather Research and Forecasting (WRF) NWP model for data over the continental US. In principle, it can be adapted to any other SW radiative transfer band model. It has been verified against a control experiment and using data from five radiometric stations in the contiguous US. The control experiment consisted of a clear-sky evaluation of the RRTMG solar radiation estimates obtained in WRF when RRTMG is driven with ground-observed aerosol optical properties. Overall, the verification has shown satisfactory results for both broadband SW surface direct and diffuse irradiances. The parameterization has proven effective in significantly reducing the prediction error and constraining the seasonal bias in clear-sky conditions to within the typical observational error expected in well maintained radiometers.

Figures

  • Table 1. Spectral bands distribution in RRTMG. From top to bottom rows, λ’s (in nm) are band mean, band minimum and band maximum values, respectively. Note the band numbering does not follow increasing or decreasing wavelength values. The band naming convention follows the RRTMG definition.
  • Table 2. Ångström exponents for the two spectral bands of the modified Ångström’s law, the aerosol mixtures and relative humidity values. Ångström exponents are computed as described in Sect. 3.1
  • Figure 1. AOD spectral scale factor interpolated using 4-point Lagrange interpolation for relative humidity values from 0 to 99 % for each RRTMG spectral band and the rural aerosol model. For the sake of comparison, the results using Eon-weighted and unweighted spectral scale factors are shown.
  • Figure 2. Parameterized SSA and ASY parameters for the rural and urban aerosol mixtures and a relative humidity of 80 % (thick line). The Shettle and Fenn (1979) spectral values are shown with cross marks. They are interpolated using cubic splines (thin line). The grey region encompasses the variability range of the parameters with different values of relative humidity.
  • Figure 3. Relative error of both the control experiment and the test cases as compared against the GHI, DNI and DIF ground observations at each site and the composite of all sites (ALL). The statistics are based on 767 samples for GHI and DIF and 892 samples for DNI. The number of samples per site varies between 150 and 200. The yellow-shaded area highlights the ±5 % error region as a rough reference of the expected observational error. The grey blocks refer to the control experiment and encompass the region around the mean relative error (horizontal black line) that contains 66 % of the experimental points at each site (33 % above the mean error and 33 % below). The relative error obtained in the test cases is indicated with the vertical bars at each site. They also encompass 66% of the experimental points, the white circle mark being the mean relative error.
  • Figure 4. Error analysis with respect to the variability range of AOD, SSA and ASY parameters for GHI, DNI and DIF that results from the 1- year WRF simulations. Panels (a–c) show the relative frequency distribution of the observed AOD at 0.55 µm, the observed and parameterized SSA values and the observed and parameterized ASY values, respectively. Panels (d–l) show the observed and simulated DNI, DIF and GHI values (upper half of the panels) as well as their relative errors (lower half of the panels) as a function of the observed AOD at 0.55 µm, SSA and ASY values. The expected observational error region for the surface solar irradiance observations, roughly estimated as ±5 %, is highlighted in yellow.
  • Figure 5. Daily mean relative error in the predicted DNI, DIF and GHI irradiances (simulated values minus observations, relative to the observations) using the RRTMG assuming rural and urban aerosol models throughout the simulated year and the composite of the five experimental sites. A 15-day moving average filter has been used to make clear the bias trend. For GHI, the calculated values with the Dudhia scheme are also shown. The expected observational error region for the surface solar irradiance observations, roughly estimated as ±5 %, is highlighted in yellow.

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Ruiz-Arias, J. A., Dudhia, J., & Gueymard, C. A. (2014). A simple parameterization of the short-wave aerosol optical properties for surface direct and diffuse irradiances assessment in a numerical weather model. Geoscientific Model Development, 7(3), 1159–1174. https://doi.org/10.5194/gmd-7-1159-2014

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